Actionable companion · Assessment & reasoning
Learning with agency: Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
Evidence and context
Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Background The aim of this study was to assess social preferences for two different advanced digital health technologies and investigate the contextual dependency of the preferences. Methods A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over. Participants were asked to imagine that they needed a total hip replacement surgery and to indicate whether they would prefer a traditional or a robot-assisted (RA) hip surgery. To better understand preferences for the chosen method, the willingness to pay (WTP) method was used. The same assessment was conducted for preferences between a radiologist’s and AI-based image analysis in establishing the radiological diagnosis of a suspected tumour. Respondents’ electronic health literacy was assessed with the eHEALS questionnaire. Descriptive methods were used to assess sample characteristics and differences between subgroups. Associations were investigated with correlation analysis and multiple linear regressions. Results Altogether, 1400 individuals (53.7% female) with a mean age of 58.3 (SD = 11.1) years filled in the survey. RA hip surgery was chosen by 762 (54.4%) respondents, but only 470 (33.6%) chose AI-based medical image evaluation. Those who opted for the digital technology had significantly higher educational levels and electronic health literacy (eHEALS). The majority of respondents were willing to pay to secure their preferred surgical (surgeon 67.2%, robot-assisted: 68.8%) and image assessment (radiologist: 70.9%; AI: 77.4%) methods, reporting similar average amounts in the first (p = 0.677), and a significantly higher average amount for radiologist vs. AI in the second task (p = 0.001). The regression showed a significant association between WTP and income, and in the hip surgery task, it also revealed an association with the type of intervention chosen. Conclusions Individuals with higher education levels seem to accept the advanced digital medical technologies more. However, the greater openness for RA surgery than for AI image assessment highlights that social preferences may depend considerably on the medical situation and the type of advanced digital technology. WTP results suggest rather firm preferences in the great majority of the cases. Determinants of preferences and real-world choices of affected patients should be further investigated in future studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-024-02470-x.
What kind of evidence is this?
Embodied learning agents · Survey / observational study
Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.
Participant ages · source passage
Methods A cross-sectional online survey was performed among the general population of Hungary aged 40 years and over.
Study methods · source passage
The present study was part of a larger survey on the knowledge about and attitudes towards implantable medical devices in the Hungarian population, details have been reported elsewhere [32]. The online cross-sectional study was conducted in July of 2021, involving a sample of the Hungarian general population aged 40 years and over. Quota sampling was applied to ensure the representativeness of the sample for sex, age, education and type of residence. Data collection was carried out by a survey company, participants were recruited from a commercial online panel. Dropout rates and the size of the sampling frame was confidential information, and have not been released by the survey company. The targeted sample size was 1400 respondents. Ethical approval was granted by the Hungarian Medical Research Council (no. IV/5651-1/2021/EKU). Respondents were informed that participation in the survey was voluntary, that their data would remain anonymous and would be used for scientific purposes only. Participants provided written informed consent before the start of the survey.
Limitations and uncertainty · source passage
One of the main areas of application is diagnostic imaging, where AI-based methods have shown promising results in terms of accuracy, sensitivity and specificity in the segmentation and interpretation of radiological images [6–8]. The use of AI in the clinical environment, however, is not without its limitations. The most common challenges include the need for time-consuming and resource-intensive user training, high hardware requirements, insufficient integration into the clinical workflow, ethical and legal implications, and the lack of or limited transparency of operation, which can lead to uncertainty about the accuracy and reliability of the results [9, 10].
Design the system before the pilot
Compare the embodied interaction with an equivalent human-led or screen-based activity. Separate the effects of novelty and social presence from learning. Review physical access, sensory needs and adult supervision.
- Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
A 90-day district pilot
- Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
- Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
- Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.
The board decision brief
Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.
Attribution and reuse
Áron Hölgyesi; Zsombor Zrubka; László Gulácsi; Petra Baji; Tamás Haidegger; Miklós Kozlovszky; Miklós Weszl; Levente Kovács; Márta Péntek. Robot-assisted surgery and artificial intelligence-based tumour diagnostics: social preferences with a representative cross-sectional survey. BMC Medical Informatics and Decision Making (2024-03-27). https://doi.org/10.1186/s12911-024-02470-x
Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.